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Record W4403518708 · doi:10.1177/26323524241288873

Adapting, implementing and evaluating a navigation intervention for older people with cancer and their family caregivers in six countries in Europe: the Horizon Europe-funded EU NAVIGATE project

2024· article· en· W4403518708 on OpenAlexaffabout
Rose Miranda, Tinne Smets, Lara Pivodic, Kenneth Chambaere, Barbara Pesut, Wendy Duggleby, Bregje D. Onwuteaka‐Philipsen, Bárbara Gomes, Peter May, Katarzyna Szczerbińska, Andrew Davies, Davide Ferraris, H. Roeline W. Pasman, Maja de Brito, Ilona Barańska, Laura Gangeri, Lieve Van den Block, Salvatore Alfieri, Kelly Ashford, Iris Beijer Veenman, Lore Decoster, Natalia Drapała, Helen Cheyne, C. Dupont, Małgorzata Filipińska, Monica Gandelli, Sean Hearne, Violetta Kijowska, Amanda Lavan, Michael A. McDonnell, Eline Naert, G Purveen, Vítor Rodrigues, Lise Rosquin, Bianca Scacciati, Sónia Silva, HE Statema, Fien Van Campe, Afke van de Plas, N Van Den Noortgate, Chelsea Vinckier, Adrianna Ziuziakowska

Bibliographic record

VenuePalliative Care and Social Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of AlbertaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersHORIZON EUROPE European Innovation CouncilFonds Wetenschappelijk Onderzoek
KeywordsIntervention (counseling)HorizonBusinessPolitical sciencePsychologyGerontologyEconomic growthMedicineNursingEconomics

Abstract

fetched live from OpenAlex

Background: Navigation interventions could support, educate and empower older people with cancer and/or their family caregivers by addressing barriers and ensuring timely access to needed services and resources throughout the continuum of supportive, palliative and end-of-life care. Objectives: European Union (EU) NAVIGATE is an interdisciplinary and cross-country Horizon Europe-funded project (2022-2027) aiming to evaluate the effectiveness, cost-effectiveness and implementation of a navigation intervention for older people with cancer and their family caregivers in Europe. EU NAVIGATE aims to advance the evidence on cancer patient navigation in Europe. Design: Adaptation, implementation and evaluation of a navigation intervention with an international pragmatic randomized controlled trial (RCT) and embedded mixed-method process evaluation at its core. A logic model guides dissemination and impact-generating strategies. EU NAVIGATE involves six experienced EU academic partners; one EU national cancer league with their affiliated academic partner; three EU dissemination partners; and a Canadian partner. Methods: ) volunteer programme to healthcare contexts in Belgium, Ireland, Italy, the Netherlands, Poland and Portugal following the new ADAPT guidance. Nav-CARE was developed over the past 15 years and supports people with declining health and their families to improve their quality of life and well-being, foster empowerment and facilitate timely and equitable access to healthcare and social services. In EU NAVIGATE, the navigation intervention is being provided by trained and mentored social workers in Poland and by trained and mentored volunteers in the other five countries. Via a pragmatic RCT with process evaluation, we implement and evaluate the navigation intervention to study its impact on older people with cancer and their family caregivers. We also aim to understand its cost-effectiveness, how to optimally implement it in different countries, and its differential effects in patient subgroups. We will also map existing cancer navigation interventions in Europe, the United States and Canada to position EU NAVIGATE within the field of navigation interventions worldwide. Conclusion: EU NAVIGATE aims to deliver high-quality evidence on a navigation intervention for older people with cancer in Europe and to develop practice and policy recommendations for sustainable implementation of navigation interventions in Europe and beyond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.401
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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